How Encapture CEO Will Robinson grew Encapture to $17.9M revenue and 50 customers in 2024.

hey guys recording this here on what is it friday the 19th maybe you're seeing this on monday at the latest but want to let you know we are almost sold out for founder comp sorry founder 500 in austin texas here in about a week uh it's gonna be an amazing event 500 b2b sas founders i'm looking at the attendee list there's almost um there's almost 60 founders with more than six seven million bucks in arr it's an incredible group of group there's over uh there's over a hundred over 150 with more than a million uh more than a million revenue it's an incredible group you don't want to miss it uh grab your hotel grab your flight grab a ticket right now i'll put the link in the bio um in the description here on youtube and i think there's only about three tickets left okay about three tickets left i'd love to see you guys there don't be bashful grab your ticket now hey folks my guest today is will robinson he's the ceo at in capture a high-growth sas platform that helps banks automatically extract important information from documents launched 20 years ago in dallas texas and capture ops companies such as wells fargo frost bank and truist save time and money by using machine learning to process large amounts of data will you ready to take us to the top let's do it nathan thanks for having me on all right you don't look like you can't be that old so this company is founded 20 years ago how old are you yeah i was not the founder that's the short answer uh so that's what's funny this company yes started back in 98 actually 24 years now and i i joined a ceo about three years ago as part of a big transformation that we made as a company the company started as a professional services company that worked and partnered with some legacy automation software companies in our industry and uh those software companies would bring us in and we would help sell and implement their software and over time we started building our own product internally to kind of fill some gaps in the market and um when i joined about three years ago we made uh kind of a big decision to pivot away from these legacy guys and focus purely on the product that we had built um over the previous decades so i had a really fortunate opportunity to step into a business with a lot of folks who knew uh what they're doing knew the market we were selling into and a lot of it was just kind of re-prioritizing um you know uh how we're going to market where we focus making sure we have the right folks on the bus and and what was a team size when you joined in 2019 just to get a sense of the operation yeah it was in the mid 30s 35 5 36 37 folks and um you know it's um yeah we're now up to 75 and uh you know and and look nathan people don't like to talk about this a lot and you know i don't say this as like a um you know this is not a badge of honor but i think transitioning the business was hard and you know um you know making sure that we have the right folks here uh was important and some folks were just like hey you know what i've been working at uh and where you were going it's not not a fit for me and so um we've had you know a lot of change kind of top to bottom in the organization to bring in people that are excited about the vision that we've set and kind of where we're going with our own product so let's fast forward to the product today right so give me a use case what's an example of a document a bank will need your software to extract data from yeah great question so i use the mortgage example a lot because most people have bought a house but when you go talk to a loan officer about buying a house and applying for a mortgage they're going to ask you for a copy of your driver's license a recent pay stub probably the last two years of your tax returns and they're building this financial profile on you to understand how much money do you make and how much of how big of a mortgage can you qualify for typically in a bank there are people in the back office that as you send in those documents they're manually typing in your data they're manually reviewing all the data to make sure it's correct it's accurate you know that if you say you make 80 000 a year your pay stub actually pencils out to an 80 000 a year income our system can come in and automate that entire process so we use machine learning make it easy to collect those documents and once we have them we can read the documents automatically we can extract the data we can do these calculations we can verify that the data is consistent across all the different documents so that people have to spend and candidly waste a lot of time doing that oh what's going on there youtube good to see you guys now imagine this you love watching these interviews with sas founders but imagine if we took all of the valuation data out from over 2807 interviews i've done manually saves you a lot of time well we've done this we've built it into the beautiful interface inside of founder path check this out i'll show you how you can access this in a second but you log in you connect your stripe account you see your valuation real time you can see what it changed over the past 88 days and even set goals for valuation this year now the secret evaluation is there's many different ways to value a sas business so the reason you're going to see three or four different valuations inside of your frowner path dashboard this is all free by the way is because depending on who's doing the buying of your sas company you're going to get a different valuation a vc is going to pay a different valuation private equity firm is different if you're going to do a minority sale that's different and if you sell the whole business that's a different valuation you can see all those when i hover over here right so the teal is what a vc would pay yellow is what private equity and red is if you sold the whole thing outright now what's cool about this is this is not built off random data again you guys hear these interviews on youtube all these datas are built from real-time valuation data points founders share with us on the show so traction 1.2 million seed round 3.7 raised they sold 22 percent of their business go in here and filter by the event maybe you only want to see companies that have sold the whole business well here are a bunch that have been acquired the valuation and the multiple maybe you're going out right now and you're raising your seed round well go in here and look at all this recent seed deals that went down what they raised what valuation they raised at and what percent that they sold there's never been a larger data set of sas valuations than what you can get now inside of founderpath and we're thrilled to bring it to you all right we're gonna go back to the youtube video here in a second but if you want to check this tool out if you want to jump in and sign up you can check it out for free to get your valuation at this link this link founderpath.com forward slash products forward slash evaluations or if you go to founderpath.com and hover over products click on get your valuation here and go ahead and sign up to give it a whirl again all that valuation data live right inside the platform i hope to see you there all right let's jump back into the interview power of ai usually is a direct correlation to the power of the the testing set that was fed the machine in the first place to learn on so it's really hard sometimes to get your your you know a grasp of a large enough testing size to make the ai actually useful what did you guys use to train the ai in the first place what was your testing cohort yeah that's a great that's a great question and it's funny um there's standard documents things like driver's license that are that are very standardized there's a lot of them it's easy to get trained up on what we call you know structured content but documents that come in the same format every time but something like a pay stub there's a lot of variety in that um the the layout where the data is where it's coming from and so you know we've been able to train on hundreds if not thousands of data sets of sample sets to really improve our machine learning and i'll tell you something else nathan kind of a secret in our world when people think of machine learning they think a lot of kind of what they see in commercials which is unsupervised machine learning where you just feed massive data sets into a system and then the system naturally gets you know understands patterns and gets smarter on its own we actually employ a supervised machine learning technique which is where we feed data to the system the system starts to look for patterns and trends and we as humans can come in and actually influence the models that we build and help either you know affirm certain decisions the the system is made or we can correct maybe bad errors or assumptions based on what we see in the data as well understood yeah and so how many on your team of 75 are full-time engineers we've got probably 25 or 30 yeah okay interesting and then i guess give me a sense of sort of how you price so what is your average customer going to pay to use this technology today yeah so we have these broad we we price based on the volume of pages the number of pages or the number of documents that come through our system so if...